Abstract: Context. The light from distant supernovae (SNe ) can be magnified through gravitational lensing when a foreground galaxy is
located along the line of sight. This line-up allows for detailed studies of SNe at high redshift that otherwise would not be possible.
Spectroscopic observations of lensed high-redshift Type Ia supernovae (SNe Ia) are of particular interest since they can be used
to test for evolution of their intrinsic properties. The use of SNe Ia for probing the cosmic expansion history has proven to be an
extremely powerful method for measuring cosmological parameters. However, if systematic redshift-dependent properties are found,
their usefulness for future surveys could be challenged.
Aims. We investigate whether the spectroscopic properties of the strongly lensed and very distant SN Ia PS1-10afx at z = 1.4, deviates
from the well-studied populations of normal SNe Ia at nearby or intermediate distance.
Methods. We created median spectra from nearby and intermediate-redshift spectroscopically normal SNe Ia from the literature at
−5 and +1 days from light-curve maximum. We then compared these median spectra to those of PS1-10afx.
Results. We do not find signs of spectral evolution in PS1-10afx. The observed deviation between PS1-10afx and the median templates
are within what is found for SNe at low and intermediate redshift. There is a noticeable broad feature centred at λ ∼ 3500 Å, which is
present only to a lesser extent in individual low- and intermediate-redshift SN Ia spectra. From a comparison with a recently developed
explosion model, we find this feature to be dominated by iron peak elements, in particular, singly ionized cobalt and chromium.Found in: osebiKeywords: supernovae: individual: PS1-10afx – gravitational lensing: strong – supernovae: generalPublished: 23.01.2018; Views: 2348; Downloads: 0 Fulltext (1,00 MB)
Abstract: The intermediate Palomar Transient Factory (ATel #4807) reports the discovery and classification of the following Type Ia SNe. Our automated candidate vetting to distinguish a real astrophysical source (1.0) from bogus artefacts (0.0) is powered by three generations of machine learning algorithms: RB2 (Brink et al. 2013MNRAS.435.1047B), RB4 (Rebbapragada et al. 2015AAS...22543402R) and RB5 (Wozniak et al. 2013AAS...22143105W).Found in: osebiKeywords: Supernovae, TransientPublished: 23.01.2018; Views: 1859; Downloads: 0 Fulltext (344,06 KB)